電子資訊產業供給鏈管理---子計畫VI：電腦與電腦週邊組裝業供應管理之研究

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(2) Supply Chain Management for PC & PC Peripheral Assembly Industry NSC 88-2213-E-009-024 87 8 1 88 7 31 !"#$% &'()*+, ,-+./01

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(18) FGª«¬ ®( ¯°±²³ ,-./H ´. purchase. In this study, we develop a discount-pricing model for customers’ advance purchase. First, we developed two discount-pricing models for manufacturers and customers, respectively. Then, the two discount-pricing models are aggregated to a single model for determining the optimal advanced purchase ratio and the feasible discount price. Keywords Assembly-to-Order,Safety-Stock, Advance Purchase, Discount Price

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(35) fBÉ%HcO1 1Û. In an assembly-to-order production system, manufacturers usually perform two strategies to reduce the impacts of customers’ demand uncertainty. One is by setting certain levels of safety stocks, and the other is by adjusting the demand forecast. In this study, we propose the additional strategy that is to stimulate customers to make advanced. 1.

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(97) 1 x !"#$%&'()*+$,"./ “*+, 01 x %” 2. 32&'()'($,4 “'(, 01” /56782 33 9:'();<=>?8!@A5BC D>?/EF 8 PHt −2,t = MAX{Ft −2, t ⋅ x% − MAX{SSt −2 + PHt −2,t −1 + PHt −3,t −1 − Ft − 2, t −1,0},0}  PHt −1,t = MAX{Ft −1, t − PHt −2, t − SSt −1,0}  PH  2, t −1, t = PHt −2,t + PHt −1,t − APt. Step.3 &GH!4 Step.2 A 8 IJK>? / LMNOP@AQ TCD (x) =PPD +OCD +SSCD +SCD T. t =1. T. ∑{P⋅ PH. 1,t − 1,t. t =1. + P2 ⋅ PH2, t −1, t } + O ⋅. T. ∑ t =1. Yt −1, t + H ⋅. T. ∑ t =1. SSt + B⋅. t =1. T. T. t =1. t =1. + H ⋅ ∑ SS t + B ⋅ ∑ Et  PH t − 2,t = MAX {Ft − 2,t ⋅ x% − MAX {SSt − 2 + PH t − 2,t −1 + PH t − 3, t − 1 − Ft − 2,t − 1,0},0}   PH t − 1, t = MAX {F t −1, t − PH t − 2,t − SSt − 1,0}  PH  2,t − 1, t = PH t − 2, t + PH t − 1, t − APt.  1, PHt −1, t > 0  1, PHt −2, t > 0 Yt −1, t =  Yt −2, t =   0, PHt −1, t ≤ 0  0, PHt − 2,t ≤ 0 SSt = MAX{SSt −1 + PHt −1,t + PHt − 2, t − Dt ,0} Et = MAX{Dt − SSt −1 − PHt −1, t − PH t −2, t ,0}. Step.1

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(115). NPS = TRS − TCS. = (P ⋅ AP) − (MP + MSS). ()*+ NPS {H12wgh TRS {H123|} TC S {H12304 P {H~N AP {H~!F MP {\04 MSS {\ RS 047. MAX NPS = TRS − TCS. = (P⋅ AP) − (MP+ MSS) = P⋅. T. U. T. U. T. t. t =1. u=1 t =1. t. t. u=1 t =1.

(116) ·1¸\¯°¹ºk I t (u) = Ot (u)+ It −1(u) − TUt (u ) ·2¸´%ºk Ot (u) = Ft −2, t ⋅ PU(u)⋅ Rl + Su − EIt −1(u). ,-*+ t {~12 t = 1 ~ T u{~12U\u = 1 ~ U . ·3¸³\¯F EIt −1(u) = I t − l (u) − Ft − 2, t ⋅ PU (u) ⋅ Rl. 4. . ∑ AP − ∑∑ PR(u)⋅ O (u) + ∑∑PR(u) ⋅ I (u) ⋅ RATE .

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(126) !"#$%& '()*+ ,-./0 1. . T. t. U. t. APt ⋅ PU(u) = TUt (u ). ∑AP − ∑∑PR(u ) ⋅ O (u) +∑∑PR(u) ⋅ I (u) ⋅ RATE t= 1. s. t.. T. T. EIt −1(u) = I t − l (u) − Ft −1,t ⋅ PU(u) ⋅ Rl. MAX NPS = TRS − TCS = (P ⋅ AP) − (MP+ MSS) t. t. Ot (u) = Ft −2, t ⋅ (1− x )⋅ PU(u)⋅ Rl + Su + PHt −2, t ⋅ PU (u) − EIt −1 (u ). 4jkÈ9ÉWÊ*ËÌÀ{ Step.1

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(139) #$%& U}~LB(Y #$%Z [q 9;Xh #$%Z[Kl9;Xh V.S.#$ %Z[`mq;=fg ∆NPS (x) 49 =eMfg ∆TC D (x ) uat9;N O>? #$%Xhfgop ?b #$ %Z [ x t|M KLU#$%Z[(9:;<= Y69; Xh S ./01 PD (x ) ≥ P * ≥ PS ( x) ' ()*+. ,-./0 [1] K.R. Baker, “An Experimental Study of Effectiveness of Rolling Schedules in Production planning,” Decision Science,. 6.

(140) Õ. Vol.8, pp.19-27, 1977. [2] H.L. Lee, V. Padmanabhan, and S. Whang, “The Bullwhip Effect in Supply Chains,” Sloan Management Review, pp.90-102, 1997. [3]

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